A Dynamic Simulation Model for a Heat Exchanger Malfunction Monitoring

Modelling and simulation is presented for a finned cross-flow heat exchanger with the aim to heat cold air to be fed to air conditioning batteries for marine purposes. The model employed in this paper is finalized to simulate the dynamic behavior of air and water temperatures fed to the air conditioning batteries operating in cold environments, in order to predict possible troubles owing to the change in input parameters, such as unwanted flow rate variations due to system malfunctions. In the investigated model, heat balance equations are presented and discretized by Laplace transform, which has the advantage to easily account for the different structures of heaters used for the purpose of validation. The model was implemented in the Matlab-Simulink environment for its high capacity of dealing with dynamic systems. The results of the model are satisfactory, as the dynamic behavior of the air stream temperature is correctly reproduced, as compared to experimental data, providing a suitable parameter for malfunctions prediction

Are conventional statistical techniques exhaustive for defining metal background concentrations in harbour sediments? A case study: The Coastal Area of Bari (Southeast Italy)

Sediment contamination by metals poses significant risks to coastal ecosystems and is considered to be problematic for dredging operations. The determination of the background values of metal and metalloid distribution based on site-specific variability is fundamental in assessing pollution levels in harbour sediments. The novelty of the present work consists of addressing the scope and limitation of analysing port sediments through the use of conventional statistical techniques (such as: linear regression analysis, construction of cumulative frequency curves and the iterative as technique), that are commonly employed for assessing Regional Geochemical Background (RGB) values in coastal sediments. This study ascertained that although the tout court use of such techniques in determining the RGB values in harbour sediments seems appropriate (the chemical-physical parameters of port sediments fit well with statistical equations), it should nevertheless be avoided because it may be misleading and can mask key aspects of the study area that can only be revealed by further investigations, such as mineralogical and multivariate statistical analyses

Identification of hot spots within harbour sediments through a new cumulative hazard index. Case study: port of Bari - Italy

While numerous quality guidelines unquestionably drive the management of harbour sediments, port managers increasingly require easy-to-use tools supporting them in assessing the sediment quality. In this work, a new hazard index, named cumulative Normalized and Weighted Average Concentration (c-NWAC), is proposed, considering the concentrations of main hazardous, toxic and bio-accumulative sediment contaminants. This index is an upgraded version of the previously introduced NWAC index, which considered only the metal concentrations. The c-NWAC values range from 0 to 10 scores, and their visualization on a colour base code map leads to an easy identification of hotspots. The applicability of the new index was verified using a dataset derived from the analyses of 42 samples collected at different depths of the seabed of the Port of Bari (Italy). The concentrations of 58 parameters were considered, namely 11 metals and metalloids, 17 congeners of polychlorinated biphenyls (PCBs), 16 congeners of polycyclic aromatic hydrocarbons (PAHs), further 11 organic micropollutants, total organic carbon (TOC) and nutrients (Ntot, Ptot). For the study area, the obtained c-NWAC values resulted well correlated to the corresponding values of the commonly used mean Effects Range Median quotient (mERMq), and similar hazard categories were developed for the new index, even if the latter do not necessarily explain the real sediment toxicity, because they were derived from a different sediment dataset and different test species. Moreover, the hazard predictive ability of the new index was verified (with satisfactory results) by testing the considered samples by eco-toxicological assays, using three different biological species, and comparing the obtained results to the corresponding hazard categories developed for c-NWAC

Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project

Introduction: Formulating reliable prognosis for ischemic stroke patients remains a challenging task. We aimed to develop an artificial intelligence model able to formulate in the first 24 h after stroke an individualized prognosis in terms of NIHSS. Patients and methods: Seven hundred ninety four acute ischemic stroke patients were divided into a training (597) and testing (197) cohort. Clinical and instrumental data were collected in the first 24 h. We evaluated the performance of four machine-learning models (Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost) in predicting NIHSS at discharge both in terms of variation between discharge and admission (regressor approach) and in terms of severity class namely NIHSS 0–5, 6–10, 11–20, >20 (classifier approach). We used Shapley Additive exPlanations values to weight features impact on predictions. Results: XGBoost emerged as the best performing model. The classifier and regressor approaches perform similarly in terms of accuracy (80% vs 75%) and f1-score (79% vs 77%) respectively. However, the regressor has higher precision (85% vs 68%) in predicting prognosis of very severe stroke patients (NIHSS > 20). NIHSS at admission and 24 hours, GCS at 24 hours, heart rate, acute ischemic lesion on CT-scan and TICI score were the most impacting features on the prediction. Discussion: Our approach, which employs an artificial intelligence based-tool, inherently able to continuously learn and improve its performance, could improve care pathway and support stroke physicians in the communication with patients and caregivers. Conclusion: XGBoost reliably predicts individualized outcome in terms of NIHSS at discharge in the first 24 hours after stroke

Multi-failure psoriasis patients: characterization of the patients and response to biological therapy in a multicenter Italian cohort

Introduction Patients with psoriasis who have failed multiple biologic drugs have been defined as "multi-failure," although there are no clear data on the characteristics, comorbidities, and best treatment strategies for this population. Nowadays, given the next generation and the number of biologics available, patients are considered multi-failure when >= 4 biologics fail to achieve a good response.Methods Demographic characteristics and efficacy of anti-interleukin drugs in multi-failure patients were compared to a cohort of general psoriatic patients treated with IL-23 or IL-17 inhibitors.Results In total 97 multi-failure patients (>= 4 lines of biologics) were compared with 1,057 patients in the general cohort. The current drugs in the multi-failure group were risankizumab (34), ixekizumab (23), guselkumab (21), brodalumab (7), tildrakizumab (5), ustekinumab (4), secukinumab (2), and certolizumab pegol (1). A significant difference was found in the multi-failure cohort for age of psoriasis onset (mean 29.7 vs. 35.1, P < 0.001), concurrent psoriatic arthritis (45.4 vs. 26.9%, P < 0.001), diabetes mellitus (30.9 vs. 10.9%, P < 0.001), and cardiovascular comorbidity (54.6 vs. 39.8%, P = 0.005). In multi-failure patients, current biological therapy showed a good initial response (PASI 90 and 100 of 41.24 and 27.84%, respectively, at 16 weeks); the response tended to decline after 40 weeks. Anti-IL-17 agents showed clinical superiority over IL-23 agents in terms of achieving PASI90 at 28 weeks (P < 0.001) and 40 weeks (P = 0.007), after which they reached a plateau. In contrast, IL-23 agents showed a slower but progressive improvement that was maintained for up to 52 weeks. A similar trend was also seen for PASI100 (28 weeks P = 0.032; 40 weeks P = 0.121).Conclusions The multi-failure patient is characterized by many comorbidities and longstanding inflammatory disease that frequently precedes the introduction of systemic biologic therapy. Further studies are needed to identify more specific criteria that could be applied as a guideline by clinicians

Dysmicrobism, inflammatory bowel disease and thyroiditis: Analysis of the literature

The human body is colonized by a large number of microbes that are collectively referred to as the microbiota. They interact with the hosting organism and some do contribute to the physiological maintenance of the general good health thru regulation of some metabolic processes while some others are essential for the synthesis of vitamins and short-chain fatty acids. The abnormal variation, in the quality and/or quantity of individual bacterial species residing in the gastro intestinal tract, is called dysmicrobism. The immune system of the host will respond to these changes at the intestinal mucosa level which could lead to Inflammatory Bowel Diseases (1BD). This inflammatory immune response could subsequently extend to other organs and systems outside the digestive tract such as the thyroid, culminating in thyroiditis. The goal of the present study is to review and analyze data reported in the literature about thyroiditis associated with inflammatory bowel diseases such as Ulcerative Colitis (UC) and Crohn's Disease (CD). It was reported that similarities of some molecular bacterial components with molecular components of the host are considered among the factors causing IBD through an autoimmune reaction which could involve other non-immune cell types. The axis dysmicrobism-IBD-Autoimmune reaction will be investigated as a possible etiopathogenic mechanism to Autoimmune Thyroiditis. If such is the case, then the employment of specific probiotic strains may represent a useful approach to moderate the immune system

Can italian healthcare administrative databases be used to compare regions with respect to compliance with standards of care for chronic diseases?

BACKGROUND: Italy has a population of 60 million and a universal coverage single-payer healthcare system, which mandates collection of healthcare administrative data in a uniform fashion throughout the country. On the other hand, organization of the health system takes place at the regional level, and local initiatives generate natural experiments. This is happening in particular in primary care, due to the need to face the growing burden of chronic diseases. Health services research can compare and evaluate local initiatives on the basis of the common healthcare administrative data.However reliability of such data in this context needs to be assessed, especially when comparing different regions of the country. In this paper we investigated the validity of healthcare administrative databases to compute indicators of compliance with standards of care for diabetes, ischaemic heart disease (IHD) and heart failure (HF). METHODS: We compared indicators estimated from healthcare administrative data collected by Local Health Authorities in five Italian regions with corresponding estimates from clinical data collected by General Practitioners (GPs). Four indicators of diagnostic follow-up (two for diabetes, one for IHD and one for HF) and four indicators of appropriate therapy (two each for IHD and HF) were considered. RESULTS: Agreement between the two data sources was very good, except for indicators of laboratory diagnostic follow-up in one region and for the indicator of bioimaging diagnostic follow-up in all regions, where measurement with administrative data underestimated quality. CONCLUSION: According to evidence presented in this study, estimating compliance with standards of care for diabetes, ischaemic heart disease and heart failure from healthcare databases is likely to produce reliable results, even though completeness of data on diagnostic procedures should be assessed first. Performing studies comparing regions using such indicators as outcomes is a promising development with potential to improve quality governance in the Italian healthcare system